COMP SCI 2103 - Algorithm Design & Data Structures for Engineers
North Terrace Campus - Semester 2 - 2016
General Course Information
Course Code COMP SCI 2103 Course Algorithm Design & Data Structures for Engineers Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 6 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 1102 or COMP SCI 1202 Incompatible COMP SCI 1103, COMP SCI 1203, COMP SCI 2004 Restrictions BE(Ch)/Ma, BE(CE)/Ma, Course Description Builds on the foundation provided by the COMP SCI 1201-1202 sequence to introduce the fundamental concepts of data structures and the algorithms that proceed from them, and aspects of software engineering. Topics include recursion, the underlying philosophy of object-oriented programming, fundamental data structures (including stacks, queues, linked lists, hash tables and trees), the basics of algorithmic analysis and an introduction to the principles of language translation
- Review of elementary programming concepts
-Fundamental data structures: Stacks; queues; linked lists
- Object-oriented programming: Object-oriented design; encapsulation and information hiding; classes; separation of behaviour and implementation; class hierarchies; inheritance; polymorphism
- Fundamental computing algorithms: O(N log N) sorting algorithms
- Recursion: The concept of recursion; recursive backtracking; implementation of recursion
- Basic algorithmic analysis: Asymptotic analysis of upper and average complexity bounds; identifying differences among best, average and worst case behaviours; big 'O', little 'o', omega and theta notation
- Algorithmic strategies: Brute-force algorithms; greedy algorithms; divide-and-conquer; backtracking; branch-and-bound; heuristics; pattern matching and string/text algorithms; numerical approximation algorithms
- Overview of programming languages: Programming paradigms
- Software evolution: Software maintenance; characteristics of maintainable software; reengineering; legacy systems; software reuse.
Course Coordinator: Dr Mingyu Guo
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesCourse Learning Outcomes
At the end of this course, students will:
1. be able to competently program in C/C++ in the OO paradigm,
2. be able to manage memory usage in C/C++ programs,
3. be able to explain fundamental computing algorithms,
4. be able to analyse algorithms and identify key algorithmic strategies,
5. be familiar with fundamental software engineering practices,
6. have an overview of programming language design issues,
7. have developed their professional writing skills,
8. have developed their problem solving skills,
9. have worked in small group and team environments,
10. have an overview of ethics in computer science,
11. understand what abstract data types are, and
12. be able to apply elementary abstract data types to solve programming problems.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4,5,6,8,11,12 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
3,4,8,12 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
7,9 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
5,7,9 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
10 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesThe textbook for this course is: Problem Solving with C++, 7th or 8th Edition, Walter Savitch
Recommended ResourcesStudents who have Java as a programming language and are entering this course are strongly encouraged to make use of the simple on-line resource that will be made available on the course website, closer to the start of term.
Online LearningIn this course, we use an online learning environment called Moodle, available at: http://forums.cs.adelaide.edu.au
Learning & Teaching Activities
Learning & Teaching ModesThe course has three contact activities: lectures, tutorials and practicals. Each of these activities will provide you with the resources necessary to understand the course material.
Lectures will present information and provide an opportunity for the introduction and discussion of programming, algorithmic and other material. You should expect to attend all of these and participate in small group work.
Tutorials will provide a small group discussion forum where you and a tutor will work through a problem set to identify key topics and give you necessary practice in formulating answers to key questions.
Practicals are an in-lab activity session where you will work on the weekly course assignments in C++, while receiving feedback from practical markers who are stationed around the lab area. You will need to explain your work to the marker to ensure that you have understood everything that we're trying to pass on.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.You are expected to allocate 3 hours per week for lectures, approximately 4 hours per week for practicals (as a minimum) and 1 hour per week (on average) for tutorials. On average, you should require no more than 10 hours per week for this course.
Learning Activities SummaryThe weekly pattern is three one-hour lectures and a two-hour practical session, with a tutorial every fortnight. The outline course content is:
Review of fundamental C/C++ programming techniques, pointer arithmetic and function pointers, memory errors and core dumps
Abstract data types and class hierarchies
Inheritance, friends, and overloading
Using classes, OO Design principles, testing and design
Principles of software re-use and maintenance, recursion
Ethics, polymorphism, using ADTs to produce usable structures
Introduction to complexity analysis, upper and lower complexity bounds, best-case and worst-case, big O, little o, omega and theta
Complexity analysis, searching and sorting Algorithms
Recursive complexity, linked lists and stacks
Queues, other linked list based data structures
Trees, algorithmic strategies
Problem solving, programming paradigms, introduction to type systems
The course is structured to take you from an introductory knowledge of C++ to a higher level, as well as addressing some key areas of computer programming and algorithm design.
The summary of the areas covered in this course are:
Review and development of previous knowledge of C++
Fundamental data structures
Fundamental Computing Algorithms
Basic Algorithmic Analysis
Overview of programming languages
Professional Skills Development
Specific Course RequirementsThere are no specific course requirements.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryThe marks for this course are made up of:
• Written Examination: 50%
• Practical Examination: 20%
• Practical Assignments: 30%
Assessment Related RequirementsTutorials do not attract marks but attendance is recorded and students are expected to attend at least 80% of tutorials. Students must achieve an overall passing mark and least 40% in the prac exam:
• Written Examination (Total 50%)
• Practical Examinations (Total 20%)
• Practical Assignments (Total 30%)
Assessment DetailEach weekly assignment is worth 3% of the final mark, to a total of 30%.
The CBOK mappings are as follows:
Abstraction Design Data and Information Programming Systems Development Prac Assignments 5 5 3 5 3 Prac Exam 3 3 3 Main Exam 3 3 3
CBOK categories are explained in section 4 of the ICT core body of knowlege. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
Below are the mappings to learning outcomes and graduate attributes
Component Weight Learning Outcomes Graduate Attributes Prac Assignments 30% 1,2,4,5,6,8,9,11,12 1,2,3,4,5,6,7 Prac Exam 20% 1,2,4,5,6,8,9,11,12 1,2,3,4,5,6,7 Main Exam 50% 1,2,3,4,5,6,7,8,9,10,11,12 1,2,3,4,5,6,7,8
SubmissionAll programming submissions must be submitted through electronic means that will be clearly identified on the assignment rubric.
The School of Computer Science observes a strict lateness policy. Extensions may be requested in advance for medical or compassionate reasons but (1) all requests must be accompanied by documentation, (2) extensions awarded will be in proportion to the time lost that is supported by documentation, (3) extensions are almost never granted on the final day unless the issue is both severe and unforeseen, and (4) extensions are never granted because you have been busy, have managed your time poorly or are overloaded in other courses.
Programming marks will be returned to you within 10 working days. If your work is considered to not be a sufficient attempt, you may be asked to resubmit the work. If we can identify that you are trending towards overall insufficient progress (and at risk of triggering the minimum performance threshold) then we may contact you to make you explicitly aware of this risk, however, you should be tracking your own progress and making your best attempt at every piece of work, rather than aiming to scrape by.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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